Carnegie Lake Phase I Lake and Watershed Restoration Plan

The U.S. Army Corps of Engineers, New York District (USACE), in partnership with the New Jersey Department of Environmental Protection (NJDEP), conducted a comprehensive study to identify ecosystem restoration and flood-damage-reduction solutions for the Millstone River Basin in New Jersey. Project stakeholders included the U.S. Geological Survey, the USDA Natural Resources Conservation Service, Mercer and Middlesex Counties, Princeton University, and the Stony Brook–Millstone Watershed Association.

During the study, Carnegie Lake, a freshwater impoundment of the Millstone River located in Mercer and Middlesex Counties, was identified as a significant natural resource within the watershed. As the project’s lead agency, USACE contracted Princeton Hydro to develop a detailed lake and watershed restoration plan for Carnegie Lake.

The Carnegie Lake and Watershed Restoration Plan focused on three primary objectives. First, it involved collecting a wide range of site‑specific in‑lake and watershed data. Second, it quantified the hydrologic and non‑point‑source pollutant budgets for the lake, including total suspended solids and the nutrients nitrogen and phosphorus. Third, it used the findings from the first two objectives to develop a comprehensive lake and watershed restoration plan.

The first objective was completed in 2003, during which extensive data were collected throughout the growing season. A major component of this effort was a detailed bathymetric survey that measured water depths and the volume of unconsolidated sediments. Additional tasks included collecting physical, chemical, and biological in‑lake data; conducting macrophyte and fisheries surveys; and collecting and analyzing baseline and stormwater samples.

Hydrologic and pollutant budgets for Carnegie Lake and its watershed were then developed using standardized and widely accepted models calibrated with the collected baseline and stormwater data. These budgets informed water‑quality models used to predict in‑lake conditions under various climatic and pollutant‑loading scenarios. All water‑quality and watershed data, along with model results, were used to evaluate and prioritize feasible, cost‑effective in‑lake and watershed management techniques aimed at improving water quality and reducing pollutant loads.

The project was finalized in March 2005.